Facial landmark detection is an important yet challenging task for real-world
computer vision applications. This paper proposes an effective and robust
approach for facial landmark detection by combining data- and model-driven
methods. Firstly, a Fully Convolutional Network (FCN) is trained to compute
response maps of all facial landmark points. Such a data-driven method could
make full use of holistic information in a facial image for global estimation
of facial landmarks. After that, the maximum points in the response maps are
fitted with a pre-trained Point Distribution Model (PDM) to generate the
initial facial shape. This model-driven method is able to correct the
inaccurate locations of outliers by considering the shape prior information.
Finally, a weighted version of Regularized Landmark Mean-Shift (RLMS) is
employed to fine-tune the facial shape iteratively. This
Estimation-Correction-Tuning process perfectly combines the advantages of the
global robustness of data-driven method (FCN), outlier correction capability of
model-driven method (PDM) and non-parametric optimization of RLMS. Results of
extensive experiments demonstrate that our approach achieves state-of-the-art
performances on challenging datasets including 300W, AFLW, AFW and COFW. The
proposed method is able to produce satisfying detection results on face images
with exaggerated expressions, large head poses, and partial occlusions.

本文介绍了一种结合数据和模型的方法来完成面部关键点的检测，即先利用全卷积网络（Fully Convolutional Network, FCN）来生成脸部所有关键点的响应图，再利用 Point Distribution Model（PDM）生成初始面部形状，最后使用加权变体的正则化关键点均值漂移（RLMS）来微调面部形状，实验表明本文提出的方法在面部表情、头部姿态和部分遮挡等挑战性数据集上都能够取得最好的表现。